Blue electrogenerated chemiluminescence from water-soluble iridium complexes containing sulfonated phenylpyridine or tetraethylene glycol derivatized triazolylpyridine ligands

Incorporating phenylpyridine- and triazolylpyridine-based ligands decorated with methylsulfonate or tetraethylene glycol (TEG) groups, a series of iridium(III) complexes has been created for green and blue electrogenerated chemiluminescence under analytically useful aqueous conditions, with tri-n-propylamine as a coreactant. The relative electrochemiluminescence (ECL) intensities of the complexes were dependent on the sensitivity of the photodetector over the wavelength range and the pulse time of the applied electrochemical potential. In terms of the integrated area of corrected ECL spectra, with a pulse time of 0.5 s, the intensities of the Ir(III) complexes were between 18 and 102 % that of [Ru(bpy)3 ](2+) (bpy=2,2'-bipyridine). However, when the intensities were measured with a typical bialkali photomultiplier tube, the signal of the most effective blue emitter, [Ir(df-ppy)2 (pt-TEG)](+) (df-ppy=2-(2,4-difluorophenyl)pyridine anion, pt-TEG=1-(2-(2-(2-(2-hydroxyethoxy)ethoxy)ethoxy)ethyl)-4-(2-pyridyl)-1,2,3-triazole), was over 1200 % that of the orange-red emitter [Ru(bpy)3 ](2+) . A combined experimental and theoretical investigation of the electrochemical and spectroscopic properties of the Ir(III) complexes indicated that the greater intensity from [Ir(df-ppy)2 (pt-TEG)](+) relative to those of the other Ir(III) complexes resulted from a combination of many factors, rather than being significantly favored in one area.

Detection of anomalous crowd behaviour using hyperspherical clustering

Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect anomalous events and objects, two types of feature coding has been proposed: spatial features and spatio-temporal features. Spatial features comprises of contrast, correlation, energy and homogeneity, which are derived from Gray Level Co-occurrence Matrix (GLCM). Spatio-temporal feature includes the time spent by an object at different locations in the scene. Hyperspherical clustering has been employed to detect the anomalies. Spatial features revealed the anomalous frames by using contrast and homogeneity measures. Loitering behaviour of the people were detected as anomalous objects using the spatio-temporal coding.

Context-dependent correlation between resting metabolic rate and daily energy expenditure in wild chipmunks

Several empirical studies have shown that variation in daily energy expenditure (DEE) and resting metabolic rate (RMR) is influenced by environmental and individual factors, but whether these shared influences are responsible for, or independent of, relationships between DEE and RMR remains unknown. The objectives of this study were to (i) simultaneously evaluate the effects of environmental and individual variables on DEE and RMR in free-ranging eastern chipmunks (Tamias striatus) and (ii) quantify the correlation between DEE and RMR before and after controlling for common sources of variation. We found that the influence of individual factors on DEE and RMR is most often shared, whereas the influence of environmental factors tends to be distinct. Both raw and mass-adjusted DEE and RMR were significantly correlated, but this correlation vanished after accounting for the shared effect of reproduction on both traits. However, within reproductive individuals, DEE and RMR remained positively correlated after accounting for all other significant covariates. The ratio of DEE to RMR was significantly higher during reproduction than at other times of the year and was negatively correlated with ambient temperature. DEE and RMR appear to be inherently correlated during reproduction, but this correlation does not persist during other, less energy-demanding periods of the annual cycle.

The correlation between stacking fault energy and the work hardening behaviour of high-mn twinning induced plasticity steel tested at various temperatures

High-Mn Twinning Induced Plasticity (TWIP) steels have superior mechanical properties, which make them promising materials in automotive industry to improve the passenger safety and the fuel consumption. The TWIP steels are characterized by high work hardening rates due to continuous mechanical twin formation during the deformation. Mechanical twinning is a unique deformation mode, which is highly governed by the stacking fault energy (SFE). The composition of steel alloy was Fe-18Mn-0.6C-1Al (wt.%) with SFE of about 25-30 mJ/m2 at room temperature. The SFE ensures the mechanical twinning to be the main deformation mechanism at room temperature. The microstructure, mechanical properties, work hardening behaviour and SFE of the steel was studied at the temperature range of ambient ≤T[°C]≤ 400°C. The mechanical properties were determined using Instron tensile testing machine with 30kN load cell and strain rate of 10-3s-1 and the work hardening behaviour curves were generated using true stress and true strain data. The microstructure after deformation at different temperatures was examined using Zeiss Supra 55VP SEM. It was found that an increase in the deformation temperature raised the SFE resulting in the deterioration of the mechanical twinning that led to decrease not only in the strength but also in the total strain of the steel. A correlation between the temperature, the SFE, the mechanical twinning, the mechanical properties and the work hardening rate was also found. © (2014) Trans Tech Publications, Switzerland.

Estimating the total energy demand for supra-maximal exercise using the VO2-power regression from an incremental exercise test

The VO_2-power regression and estimated total energy demand for a 6-minute supramaximal exercise test was predicted from a continuous incremental exercise test. Sub-maximal VO_2- power co-ordinates were established from the last 40 seconds(s) of 150-second exercise stages. The precision of the estimated total energy demand was determined using the 95% confidence interval (95% CI) of the estimated total energy demand. The linearity of the individual VO_2-power regression equations was determined using Pearson's correlation coefficient. The mean 95% CI of the estimated total energy demand was 5.9±2.5 mL O₂ Eq•^-1kg•min^-1, and the mean correlation coefficient was 0.9942±0.0042. The current study contends that the sub-maximal VO_2-power co-ordinates from a continuous incremental exercise test can be used to estimate supra-maximal energy demand without compromising the precision of the accumulated oxygen deficit (AOD) method.

The importance of particle size in porous titanium and nonporous counterparts for surface energy and its impact on apatite formation

The importance of particle size in titanium (Ti) fabricated by powder metallurgy for the surface energy and its impact on the apatite formation was investigated. Four sorts of Ti powders of different mean particle size were realized through 20 min, 2 h, 5 h and 8 h of ball milling, respectively. Each sort of Ti powder was used to fabricate porous Ti and its nonporous counterparts sharing similar surface morphology, grain size and chemical composition, and then alkali-heat treatment was conducted on them. Surface energy was measured on the surfaces of the nonporous Ti counterparts due to the difficulty in measuring the porous surfaces directly. The surface energy increase on the alkali-heat-treated porous and nonporous Ti was observed due to the decrease in the particle size of the Ti powders and the presence of Ti–OH groups brought by the alkali-heat treatment. The apatite-inducing ability of the alkali-heat-treated porous and nonporous Ti with different surface energy values was evaluated in modified simulated body fluid and results indicated that there was a strong correlation between the apatite-inducing ability and the surface energy. The alkali-heat-treated porous and nonporous Ti discs prepared from the powders with an average particle size of 5.89 ± 0.76 μm possessed the highest surface energy and the best apatite-inducing ability when compared to the samples produced from the powders with the average particle size varying from 19.79 ± 0.31 to 10.25 ± 0.39 μm.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

The acute effects of caffeine in a sugar-sweetened beverage on energy consumption

Background: It has been suggested that those who are habitually high caffeine consumers ingest greater quantities of snack foods both in and outside the laboratory. Sugar-sweetened beverages (SSBs) are a major contributor to caffeine consumption and evidence links SSB consumption with poor dietary intake.

Objective: To determine whether varying the concentration of caffeine in SSBs influences snack food consumption and energy intake.

Methods: Caffeine taste thresholds were assessed using the International Standards Organization method for assessing taste sensitivity. In a crossover study design, participants (n=23, 26±5 years old, 58% female) were provided with a standardized meal on 4 days and simultaneously consumed SSBs with varied levels of caffeine (0, 0.67, 1.16, and 1.65 mM). The intake of food and beverage was recorded following each meal session.

Results: A one way between groups analysis of variance revealed no significant main effect of caffeine concentration on consumption of SSBs [F (3, 92)=0.154, p=0.927] or food [F (3, 92)=0.305, p=0.822]. Pearson correlation analysis identified no significant correlations between the amount of food and SSB consumed (R=−0.031–0.415, p=0.062–0.893), or the amount of food and SSB consumed with body mass index and waist circumference (R=0.000 to −0.380, p=0.073–0.999). An individual's oral sensitivity to caffeine was not associated with SSB consumption (R=0.045 to −0.309, p=0.152–0.839) or the consumption of food (R=−0.052 to −0.327, p=0.128–0.812).

Conclusions: The concentration of caffeine in SSBs did not influence the amount of food or SSB consumed.

Performance, personality, and energetics : correlation, causation, and mechanism

The study of phenotypic evolution should be an integrative endeavor that combines different approaches and crosses disciplinary and phylogenetic boundaries to consider complex traits and organisms that historically have been studied in isolation from each other. Analyses of individual variation within populations can act to bridge studies focused at the levels of morphology, physiology, biochemistry, organismal performance, behavior, and life history. For example, the study of individual variation recently facilitated the integration of behavior into the concept of a pace-of-life syndrome and effectively linked the field of energetics with research on animal personality. Here, we illustrate how studies on the pace-of-life syndrome and the energetics of personality can be integrated within a physiology-performance-behavior-fitness paradigm that includes consideration of ecological context. We first introduce key concepts and definitions and then review the rapidly expanding literature on the links between energy metabolism and personality traits commonly studied in nonhuman animals (activity, exploration, boldness, aggressiveness, sociability). We highlight some empirical literature involving mammals and squamates that demonstrates how emerging fields can develop in rather disparate ways because of historical accidents and/or particularities of different kinds of organisms. We then briefly discuss potentially interesting avenues for future conceptual and empirical research in relation to motivation, intraindividual variation, and mechanisms underlying trait correlations. The integration of performance traits within the pace-of-life-syndrome concept has the potential to fill a logical gap between the context dependency of selection and how energetics and personality are expected to interrelate. Studies of how performance abilities and/or aspects of Darwinian fitness relate to both metabolic rate and personality traits are particularly lacking.

Genetic correlation between resting metabolic rate and exploratory behaviour in deer mice (Peromyscus maniculatus)

According to the ‘pace-of-life’ syndrome hypothesis, differences in resting metabolic rate (RMR) should be genetically associated with exploratory behaviour. A large number of studies reported significant heritability for both RMR and exploratory behaviour, but the genetic correlation between the two has yet to be documented. We used a quantitative genetic approach to decompose the phenotypic (co)variance of several metabolic and behavioural measures into components of additive genetic, common environment and permanent environment variance in captive deer mice. We found significant additive genetic variance for two mass-independent metabolic measures (RMR and the average metabolic rate throughout the respirometry run) and two behavioural measures (time spent in centre and distance moved in a novel environment). We also detected positive additive genetic correlation between mass-independent RMR and distance moved (rA = 0.78 ± 0.23). Our results suggest that RMR and exploratory behaviour are functionally integrated traits in deer mice, providing empirical support for one of the connections within the pace-of-life syndrome hypothesis.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

The correlation between supermarket size and national obesity prevalence

BACKGROUND: Supermarkets provide healthy and affordable food options while simultaneously heavily promoting energy-dense, nutrient-poor foods and drinks. Store size may impact body weight via multiple mechanisms. Large stores encourage purchasing of more food in a single visit, and in larger packages. In addition they provide greater product choice (usually at lower prices) and allow greater exposure to foods of all types. These characteristics may promote purchasing and consumption. Our objective was to assess the relationship between supermarket size and obesity, which has rarely been assessed. RESULTS: Data on supermarket size (measured as total aisle length in metres) was from 170 stores in eight developed countries with Western-style diets. Data for national obesity prevalence was obtained from the UK National Obesity Observatory. We found a strong correlation between average store size and national obesity prevalence (r = 0.96). CONCLUSIONS: Explanations for the association between store size and national obesity prevalence may include larger and less frequent shopping trips and greater choice and exposure to foods in countries with larger stores. Large supermarkets may represent a food system that focuses on quantity ahead of quality and therefore may be an important and novel environmental indicator of a pattern of behaviour that encourages obesity.